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The purpose of this paper is to develop an algorithm and software for determining the size of a line-start permanent magnet synchronous motor (LSPMSMs) based on its optimization.

The software consists of an optimization procedure that cooperates with a FEM model to provide the desired behavior of the motor under consideration. The proposed improved version of the genetic algorithm has modifications enabling efficient optimization of LSPMSMs. The objective function consists of three important functional parameters describing the designed machine. The 2-D field-circuit mathematical model of the dynamics operation of the LSPMSMs consists of transient electromagnetic field equations, equations describing electric windings and mechanical motion equations. The model has been developed in the ANSYS Maxwell environment.

In this proposed approach, the set of design variables contains the variables describing the stator and rotor structure. The improved procedure of the optimization algorithm makes it possible to find an optimal motor structure with correct synchronization properties. The proposed modifications make the optimization procedure faster and more

This proposed approach can be successfully applied to solve the design problems of LSPMSMs.

The purpose of this paper is to provide an analysis of the performance of a new five-phase doubly fed induction generator (DFIG).

This paper presents the results of a research work related to five-phase DFIG framing, including the development of an analytical model, FEM analysis as well as the results of laboratory tests of the prototype. The proposed behavioral level analytical model is based on the winding function approach. The developed DFIG model was used at the design stage to simulate the generator’s no-load and load state. Then, the results of the FEM analysis were shown and compared with the results of laboratory tests of selected DFIG operating states.

The paper provides the results of analytical and FEM simulation and measurement tests of the new five-phase dual-feed induction generator. The use of the MATLAB Simscape modeling language allows for easy and quick implementation of the model. Design assumptions and analytical model-based analysis have been verified using FEM analysis and measurements performed on the prototype. The results of the presented research validate the design process as well as show the five-phase winding design advantage over the three-phase solution regarding the control winding power quality.

The main disadvantage of the winding function approach-based model development is the simplification regarding omitting the tangential airgap flux density component. However, this fault only applies to large airgap machines and is insignificant in induction machines. The results of the DFIG analyses were limited to the basic operating states of the generator, i.e. the no-load state, the inductive and resistive load.

The novel DFIG with five phase rotor control winding can operate as a regular three-phase machine in an electric power generation system and allows for improved control winding power quality of the proposed electrical energy generation system. This increase in power quality is due to the rotor control windings inverter-based PWM supply voltage, which operates with a wider per-phase supply voltage range than a three-phase system. This phenomenon was quantified using control winding current harmonic analysis.

The paper provides the results of analytical and FEM simulation and measurement tests of the new five-phase dual-feed induction generator.

Exoskeletons are mechanical structures that humans can wear to increase their strength and endurance. The purpose of this paper is to explain how exoskeletons can be used to improve performance across five phases of manufacturing.

Multivocal literature review, encompassing scientific literature and the grey literature of online reports, etc., to inform comprehensive, comparative and critical analyses of the potential of exoskeletons to improve manufacturing performance.

There are at least eight different types of exoskeletons that can be used to improve human strength and endurance in manual work during different phases of production. However, exoskeletons can have the unintended negative consequence of reducing human flexibility leading to new sources of musculoskeletal disorders (MSD) and accidents.

Findings are relevant to function allocation research concerned with manual production work. In particular, exoskeletons could exacerbate the traditional trade-off between human flexibility and robot consistency by making human workers less flexible.

The introduction of exoskeletons requires careful health and safety planning if exoskeletons are to improve human strength and endurance without introducing new sources of MSD and accidents.

The originality of this paper is that it provides detailed information about a new manufacturing technology: exoskeletons. The value of this paper is that it provides information that is comprehensive, comparative and critical about exoskeletons as a potential alternative to robotics across five phases of manufacturing.

Cross-docking is a supply chain distribution and logistics strategy for which less-than-truckload shipments are consolidated into full-truckload shipments. Goods are stored up to a maximum of 24 hours in a cross-docking terminal. In this chapter, we build on the literature review by Ladier and Alpan (2016), who reviewed cross-docking research and conducted interviews with cross-docking managers to find research gaps and provide recommendations for future research. We conduct a systematic literature review, following the framework by Ladier and Alpan (2016), on cross-docking literature from 2015 up to 2020. We focus on papers that consider the intersection of research and industry, e.g., case studies or studies presenting real-world data. We investigate whether the research has changed according to the recommendations of Ladier and Alpan (2016). Additionally, we examine the adoption of Industry 4.0 practices in cross-docking research, e.g., related to features of the physical internet, the Internet of Things and cyber-physical systems in cross-docking methodologies or case studies. We conclude that only small adaptations have been done based on the recommendations of Ladier and Alpan (2016), but we see growing attention for Industry 4.0 concepts in cross-docking, especially for physical internet hubs.

With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing.

The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration.

AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances.

This research conducts a thorough analysis of artificial neural network applications in the textile sector.

The sustainable transition towards the circular economy requires the effective use of artificial intelligence (AI) and information technology (IT) techniques. As the sustainability targets for 2030–2050 increasingly become a tougher challenge, society, company managers and policymakers require more support from AI and IT in general. How can the AI-based and IT-based smart decision-support tools help implementation of circular economy principles from micro to macro scales?

This chapter provides a conceptual framework about the current status and future development of smart decision-support tools for facilitating the circular transition of smart industry, focussing on the implementation of the industrial symbiosis (IS) practice. IS, which is aimed at replacing production inputs of one company with wastes generated by a different company, is considered as a promising strategy towards closing the material, energy and waste loops. Based on the principles of a circular economy, the utility of such practices to close resource loops is analyzed from a functional and operational perspective. For each life cycle phase of IS businesses – e.g., opportunity identification for symbiotic business, assessment of the symbiotic business and sustainable operations of the business – the role played by decision-support tools is described and embedding smartness in these tools is discussed.

Based on the review of available tools and theoretical contributions in the field of IS, the characteristics, functionalities and utilities of smart decision-support tools are discussed within a circular economy transition framework. Tools based on recommender algorithms, machine learning techniques, multi-agent systems and life cycle analysis are critically assessed. Potential improvements are suggested for the resilience and sustainability of a smart circular transition.

The purpose of this paper is to survey existing information security policy (ISP) management research to scrutinise the extent to which manual and computerised support has been suggested, and the way in which the suggested support has been brought about.

The results are based on a literature review of ISP management research published between 1990 and 2017.

Existing research has focused mostly on manual support for managing ISPs. Very few papers have considered computerised support. The entire complexity of the ISP management process has received little attention. Existing research has not focused much on the interaction between the different ISP management phases. Few research methods have been used extensively and intervention-oriented research is rare.

Future research should to a larger extent address the interaction between the ISP management phases, apply more intervention research to develop computerised support for ISP management, investigate to what extent computerised support can enhance integration of ISP management phases and reduce the complexity of such a management process.

The limited focus on computerised support for ISP management affects the kind of advice and artefacts the research community can offer to practitioners.

Today, there are no literature reviews on to what extent computerised support the ISP management process. Findings on how the complexity of ISP management has been addressed and the research methods used extend beyond the existing knowledge base, allowing for a critical discussion of existing research and future research needs.

Various organizational landscapes have evolved to improve their business processes, increase production speed and reduce the cost of distribution and have integrated their Internet with small and medium scale enterprises (SMEs) and third-party vendors to improve business growth and increase global market share, including changing organizational requirements and business process collaborations. Benefits include a reduction in the cost of production, online services, online payments, product distribution channels and delivery in a supply chain environment. However, the integration has led to an exponential increase in cybercrimes, with adversaries using various attack methods to penetrate and exploit the organizational network. Thus, identifying the attack vectors in the event of cyberattacks is very important in mitigating cybercrimes effectively and has become inevitable. However, the invincibility nature of cybercrimes makes it challenging to detect and predict the threat probabilities and the cascading impact in an evolving organization landscape leading to malware, ransomware, data theft and denial of service attacks, among others. The paper explores the cybercrime threat landscape, considers the impact of the attacks and identifies mitigating circumstances to improve security controls in an evolving organizational landscape.

The approach follows two main cybercrime framework design principles that focus on existing attack detection phases and proposes a cybercrime mitigation framework (CCMF) that uses detect, assess, analyze, evaluate and respond phases and subphases to reduce the attack surface. The methods and implementation processes were derived by identifying an organizational goal, attack vectors, threat landscape, identification of attacks and models and validation of framework standards to improve security. The novelty contribution of this paper is threefold: first, the authors explore the existing threat landscapes, various cybercrimes, models and the methods that adversaries are deploying on organizations. Second, the authors propose a threat model required for mitigating the risk factors. Finally, the authors recommend control mechanisms in line with security standards to improve security.

The results show that cybercrimes can be mitigated using a CCMF to detect, assess, analyze, evaluate and respond to cybercrimes to improve security in an evolving organizational threat landscape.

The paper does not consider the organizational size between large organizations and SMEs. The challenges facing the evolving organizational threat landscape include vulnerabilities brought about by the integrations of various network nodes. Factor influencing these vulnerabilities includes inadequate threat intelligence gathering, a lack of third-party auditing and inadequate control mechanisms leading to various manipulations, exploitations, exfiltration and obfuscations.

Attack methods are applied to a case study for the implementation to evaluate the model based on the design principles. Inadequate cyber threat intelligence (CTI) gathering, inadequate attack modeling and security misconfigurations are some of the key factors leading to practical implications in mitigating cybercrimes.

There are no social implications; however, cybercrimes have severe consequences for organizations and third-party vendors that integrate their network systems, leading to legal and reputational damage.

The paper’s originality considers mitigating cybercrimes in an evolving organization landscape that requires strategic, tactical and operational management imperative using the proposed framework phases, including detect, assess, analyze, evaluate and respond phases and subphases to reduce the attack surface, which is currently inadequate.

This paper aims to focus on 11 digital technologies (i.e. building information modeling, artificial intelligence and machine learning, 3D scanning, sensors, robots/automation, digital twin, virtual reality, 3D printing, drones, cloud computing and self-driving vehicles) that are portrayed in future trend reports and hype curves. The study concentrates on the current usage and knowledge of digital technologies in the Swedish architecture, engineering and construction (AEC) industry to gain an insight in the possible expectations and future trajectory of these digital technologies.

The study applies an abductive approach which is based on three different types of methods. These methods are a literature and document study which focused on 11 digital technologies, two workshops with industry (13 participants) and an online survey (N = 84).

The paper contributes to a current state analysis of the Swedish AEC industry concerning digital technologies and discusses the trajectory of these technologies for the AEC industry. The paper identifies hype factors, in which the knowledge of a digital technology is related to its usage. From the hype factors, four zones that show different stages of digital technology usage and maturity in the industry are induced.

The contribution of the paper is twofold. The paper shows insight into opportunities, the current barriers, use and knowledge of digital technologies for the different actors in the AEC industry. Furthermore, the study shows that the AEC industry is behind the traditional Gartner hype curves and contributes with defining four zones for digital technologies for the Swedish AEC industry: confusion, excitement, experimentation and integration.